• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.4
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• pp.347-352
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• 2002

This paper propose the Fuzzy AHP(Analytic Hierarchical Process)-based negotiation support (FAHP-NEGO) mechanism to support the dynamic negotiation process in Electronic Commerce(EC). Negotiation is a form of decision-making with two or more actively involved agents who can not make decisions independently, and therefore must make concessions to achieve a compromise. Having concerned that point, the theoretical framework of FAHP-NEGO mechanism is presented by means of fuzzy membership functions and AHP. This mechanism encompasses both qualitative and quantitative conditions, and the use of multiple negotiation procedures for solving the electronic negotiation problem, adjusting the fuzzy membership function, and restructuring the problem representation. A hypothetical example of a healthcare products purchase is given to illustrate the quality of the proposed mechanism. The results showed that the Fuzzy AHP-based negotiation support mechanism could reflect both qualitative and quantitative conditions in EC. The implications of the study for future directions of research on electronic negotiation support modeling and systems are presented.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.276-282
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• 2006

본 논문에서는 RFID 기술과 지능형 에이전트를 이용하여 고객의 위치를 실시간으로 검출하고 각 구역별 유효쇼핑정보를 계산하여 고객의 쇼핑 동선을 효율적으로 분석할 수 있는 RFID기반 쇼핑동선 분석 시스템을 제안한다. 쇼핑 카트와 장바구니에 RFID 태그를 부착하고 고객의 실시간 위치를 상품 진열대에 설치된 RFID 리더와 안테나를 통해 파악한다. 파악된 고객의 쇼핑 위치와 각 상품군에서 소비한 시간 정보 및 구매정보를 유효 쇼핑시간 계산과 동선 보정 알고리즘에 적용하여 보다 정확하고 신뢰성 있는 쇼핑동선 정보를 생성한다.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.147-168
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• 2011

As personalized recommendation of products and services is rapidly growing in importance, a number of studies provided fundamental knowledge and techniques for developing recommendation systems. Among them, the CF technique has been most widely used and has proven to be useful in many practices. However, current collaborative filtering (CF) technique has still considerable rooms for improving the effectiveness of recommendation systems: 1) a similarity function most systems use to find so-called like-minded people is not well defined in that similarity is computed from a single perspective of similarity concept; and 2) temporal information that contains the changing preference of customers needs to be taken into account when making recommendations. We hypothesize that integration of multiple aspects of similarity and utilization of temporal information will improve the accuracy of recommendations. The objective of this paper is to test the hypothesis through a series of experiments using MovieLens data. The experimental results show that the proposed recommendation system highly outperforms the conventional CF-based systems, confirming our hypothesis.

• Journal of Intelligence and Information Systems
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• v.14 no.2
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• pp.103-119
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• 2008

Traditional market which is characterized by the folksy retailing market has lost its competitiveness rapidly due to the emergence of the Internet and the change of customer's purchasing behavior. The recession of the traditional market contracts the regional economy. We suggest a u-Market, a traditional market with ubiquitous computing capability, to revitalize traditional market. The suggested u-Market system applies ubiquitous computing technologies characterized by communications between customers and objects without limitations of time and location. The proposed u-Market system offers location information and specific contents of traditional market to customers. Furthermore, u-Market system recommends the store and product list that customers are likely to visit and purchase based on their contexts, so they can save their time and effort to search the products or contents.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.119-138
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• 2016

Since the Industrial Revolution, which made the mass production and mass distribution of standardized goods possible, machine-made (manufactured) products have accounted for the majority of the market. However, in recent years, the phenomenon of purchasing even more expensive handmade products has become a noticeable trend as consumers have started to acknowledge the value of handmade products, such as the craftsman's commitment, belief in their quality and scarcity, and the sense of self-esteem from having them,. Consumer interest in these handmade products has shown explosive growth and has been coupled with the recent development of three-dimensional (3D) printing technologies. Etsy.com is the world's largest online handmade platform. It is no different from any other online platform; it provides an online market where buyers and sellers virtually meet to share information and transact business. However, Etsy.com is different in that shops within this platform only deal with handmade products in a variety of categories, ranging from jewelry to toys. Since its establishment in 2005, despite being limited to handmade products, Etsy.com has enjoyed rapid growth in membership, transaction volume, and revenue. Most recently in April 2015, it raised funds through an initial public offering (IPO) of more than 1.8 billion USD, which demonstrates the huge potential of online handmade platforms. After the success of Etsy.com, various types of online handmade platforms such as Handmade at Amazon, ArtFire, DaWanda, and Craft is ART have emerged and are now competing with each other, at the same time, which has increased the size of the market. According to Deloitte's 2015 holiday survey on which types of gifts the respondents plan to buy during the holiday season, about 16% of U.S. consumers chose "homemade or craft items (e.g., Etsy purchase)," which was the same rate as those for the computer game and shoes categories. This indicates that consumer interests in online handmade platforms will continue to rise in the future. However, this high interest in the market for handmade products and their platforms has not yet led to academic research. Most extant studies have only focused on machine-made products and intelligent services for them. This indicates a lack of studies on handmade products and their intelligent services on virtual platforms. Therefore, this study used signaling theory and prior research on the effects of sellers' characteristics on their performance (e.g., total sales and price premiums) in the buyer-seller relationship to identify the key influencing e-Image factors (e.g., reputation, size, information sharing, and length of relationship). Then, their impacts on the performance of shops within the online handmade platform were empirically examined; the dataset was collected from Etsy.com through the application of web harvesting technology. The results from the structural equation modeling revealed that the reputation, size, and information sharing have significant effects on the total sales, while the reputation and length of relationship influence price premiums. This study extended the online platform research into online handmade platform research by identifying key influencing e-Image factors on within-platform shop's total sales and price premiums based on signaling theory and then performed a statistical investigation. These findings are expected to be a stepping stone for future studies on intelligent online handmade services as well as handmade products themselves. Furthermore, the findings of the study provide online handmade platform operators with practical guidelines on how to implement intelligent online handmade services. They should also help shop managers build their marketing strategies in a more specific and effective manner by suggesting key influencing e-Image factors. The results of this study should contribute to the vitalization of intelligent online handmade services by providing clues on how to maximize within-platform shops' total sales and price premiums.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.05a
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• pp.149-166
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• 2002

국내·외 사회 전 분야의 급속한 전자상거래 발전에 따라 해운·항만 분야에도 인터넷 사업의 진출기회가 확대되고 전략적 활동이 증가하고 있다. 그 중에서도 인터넷을 기반으로 세계가 하나의 시장으로 통합되는 경향을 보이고 있어 기업의 활동 범위가 광역화되고 있으며, 시간과 장소의 통합이 기업간 거래에서 중요시 되고 있다. 지금 세계 각 국은 해상연계 물류, 무역 등 물품의 중개 관련 사이트 및 선박 운송에 따른 각종 해운관련 서비스를 가상 공간에서 제공하는 사이버 해운 시장의 선점 및 구축에 모든 힘을 쏟고 있다. 해상 운송에 따른 각종 수송서비스를 생산, 공급하는 경제활동을 해운 활동이라 한다. 해운 시장의 불확실성, 다변성, 국제성, 개방성을 특성으로 하는 해운 거래는 전자상거래를 통해 효율적으로 처리될 수 있다. 즉, 해운 거래의 비용 감소와 양질의 서비스로 선주, 화주 등 거래 당사자들의 만족도를 높일 수가 있다. 이에 따라 국내에서도 오프라인상의 해운 거래소가 사이버 해운거래소로 옮겨질 예정이다. 가상 공간을 통한 해운 거래의 구체적인 장점은 다음과 같다. 구매업체는 기존 공급업체에 대한 접근 및 새로운 공급업체의 확보가 용이하며, 경쟁 입찰 등을 통해 저렴한 비용으로 물품을 구입할 수 있다. 판매 업체의 경우 채널 확장이 가능하며 판매비를 절감할 수 있다. 또한 e-Marketplace의 입장에서 보면 해운 산업 전체를 위한 새로운 시장을 형성할 수 있으며, 이를 통해 지속적인 수익 창출도 가능하다. 이러한 해운 거래의 B2B e-Marketplace의 출현은 향후 해운 거래의 새로운 패러다임으로 자리 잡을 것이다. 사이버 해운 거래소는 선박 매매와 용선, 화물 거래를 위한 선·화주의 연결, 표준화된 카탈로그 구축, 각종 전자문서 생성, 전자 결제, 온라인 보험 가입, 해운 선용품 판매 및 관련 정보 제공 등 해운 거래를 위한 종합적인 서비스가 제공되어야 한다. 이를 위해, 본문에서는 e-Marketplace의 효율적인 연계 방안에 대해 해운 관련 업종별로 제시하고 있다. 리스트 제공형, 중개형, 협력형, 보완형, 정보 연계형 등이 있는데, 이는 해운 분야에서 사이버 해운 거래가 가지는 문제점들을 보완하고 업종간 협업체제를 이루어 원활한 거래를 유도할 것이다. 그리하여 우리나라가 동북아 지역뿐만 아니라 세계적인 해운 국가 및 물류 ·정보 중심지로 성장할 수 있는 여건을 구축하는데 기여할 것이다.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.175-191
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• 2011

With the MICE(Meeting, Incentive travel, Convention, Exhibition) industry coming into the spotlight, there has been a growing interest in the domestic exhibition industry. Accordingly, in Korea, various studies of the industry are being conducted to enhance exhibition performance as in the United States or Europe. Some studies are focusing particularly on analyzing visiting patterns of exhibition visitors using intelligent information technology in consideration of the variations in effects of watching exhibitions according to the exhibitory environment or technique, thereby understanding visitors and, furthermore, drawing the correlations between exhibiting businesses and improving exhibition performance. However, previous studies related to booth recommendation systems only discussed the accuracy of recommendation in the aspect of a system rather than determining changes in visitors' behavior or perception by recommendation. A booth recommendation system enables visitors to visit unplanned exhibition booths by recommending visitors suitable ones based on information about visitors' visits. Meanwhile, some visitors may be satisfied with their unplanned visits, while others may consider the recommending process to be cumbersome or obstructive to their free observation. In the latter case, the exhibition is likely to produce worse results compared to when visitors are allowed to freely observe the exhibition. Thus, in order to apply a booth recommendation system to exhibition halls, the factors affecting the performance of the system should be generally examined, and the effects of the system on visitors' unplanned visiting behavior should be carefully studied. As such, this study aims to determine the factors that affect the performance of a booth recommendation system by reviewing theories and literature and to examine the effects of visitors' perceived performance of the system on their satisfaction of unplanned behavior and intention to reuse the system. Toward this end, the unplanned behavior theory was adopted as the theoretical framework. Unplanned behavior can be defined as "behavior that is done by consumers without any prearranged plan". Thus far, consumers' unplanned behavior has been studied in various fields. The field of marketing, in particular, has focused on unplanned purchasing among various types of unplanned behavior, which has been often confused with impulsive purchasing. Nevertheless, the two are different from each other; while impulsive purchasing means strong, continuous urges to purchase things, unplanned purchasing is behavior with purchasing decisions that are made inside a store, not before going into one. In other words, all impulsive purchases are unplanned, but not all unplanned purchases are impulsive. Then why do consumers engage in unplanned behavior? Regarding this question, many scholars have made many suggestions, but there has been a consensus that it is because consumers have enough flexibility to change their plans in the middle instead of developing plans thoroughly. In other words, if unplanned behavior costs much, it will be difficult for consumers to change their prearranged plans. In the case of the exhibition hall examined in this study, visitors learn the programs of the hall and plan which booth to visit in advance. This is because it is practically impossible for visitors to visit all of the various booths that an exhibition operates due to their limited time. Therefore, if the booth recommendation system proposed in this study recommends visitors booths that they may like, they can change their plans and visit the recommended booths. Such visiting behavior can be regarded similarly to consumers' visit to a store or tourists' unplanned behavior in a tourist spot and can be understand in the same context as the recent increase in tourism consumers' unplanned behavior influenced by information devices. Thus, the following research model was established. This research model uses visitors' perceived performance of a booth recommendation system as the parameter, and the factors affecting the performance include trust in the system, exhibition visitors' knowledge levels, expected personalization of the system, and the system's threat to freedom. In addition, the causal relation between visitors' satisfaction of their perceived performance of the system and unplanned behavior and their intention to reuse the system was determined. While doing so, trust in the booth recommendation system consisted of 2nd order factors such as competence, benevolence, and integrity, while the other factors consisted of 1st order factors. In order to verify this model, a booth recommendation system was developed to be tested in 2011 DMC Culture Open, and 101 visitors were empirically studied and analyzed. The results are as follows. First, visitors' trust was the most important factor in the booth recommendation system, and the visitors who used the system perceived its performance as a success based on their trust. Second, visitors' knowledge levels also had significant effects on the performance of the system, which indicates that the performance of a recommendation system requires an advance understanding. In other words, visitors with higher levels of understanding of the exhibition hall learned better the usefulness of the booth recommendation system. Third, expected personalization did not have significant effects, which is a different result from previous studies' results. This is presumably because the booth recommendation system used in this study did not provide enough personalized services. Fourth, the recommendation information provided by the booth recommendation system was not considered to threaten or restrict one's freedom, which means it is valuable in terms of usefulness. Lastly, high performance of the booth recommendation system led to visitors' high satisfaction levels of unplanned behavior and intention to reuse the system. To sum up, in order to analyze the effects of a booth recommendation system on visitors' unplanned visits to a booth, empirical data were examined based on the unplanned behavior theory and, accordingly, useful suggestions for the establishment and design of future booth recommendation systems were made. In the future, further examination should be conducted through elaborate survey questions and survey objects.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.185-190
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• 2006

Most systems in ubiquitous computing analyze context information of users which have similar propensity with demographics methods and collaborative filtering to provide personalized recommendation services. The systems have mostly used static context information such as sex, age, job, and purchase history. However the systems have limitation to analyze users' propensity accurately and to provide personalized recommendation services in real-time, because they have difficulty in considering users situation as moving path. In this paper we use users' moving path of dynamic context to consider users situation. For the prediction accuracy we complete with a path completion algorithm to moving path which is inputted to RSOM. We train the moving path to be completed by RSOM, analyze users' moving pattern and predict a future moving path. Then we recommend the nearest product on the prediction path with users' high preference in real-time. As the experimental result, MAE is lower than 0.5 averagely and we confirmed our method can predict users moving path correctly.

• Journal of Intelligence and Information Systems
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• v.9 no.1
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• pp.179-193
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• 2003

Clustering is a data mining method which help discovering interesting data groups in large databases. In traditional data clustering, similarity between objects in the cluster is measured by pairwise similarity of objects. But we devise an advanced measurement called item similarity in this paper, in terms of nature of clustering transaction data and use this measurement to perform clustering. This new algorithm show the similarity by accepting the concept of relationship between different attributes. With this item similarity measurement, we develop an efficient clustering algorithm for target marketing in each group.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.101-116
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• 2015

Recently, due to the development of smart devices and social media, vast amounts of information with the various forms were accumulated. Particularly, considerable research efforts are being directed towards analyzing unstructured big data to resolve various social problems. Accordingly, focus of data-driven decision-making is being moved from structured data analysis to unstructured one. Also, in the field of recommendation system, which is the typical area of data-driven decision-making, the need of using unstructured data has been steadily increased to improve system performance. Approaches to improve the performance of recommendation systems can be found in two aspects- improving algorithms and acquiring useful data with high quality. Traditionally, most efforts to improve the performance of recommendation system were made by the former approach, while the latter approach has not attracted much attention relatively. In this sense, efforts to utilize unstructured data from variable sources are very timely and necessary. Particularly, as the interests of users are directly connected with their needs, identifying the interests of the user through unstructured big data analysis can be a crew for improving performance of recommendation systems. In this sense, this study proposes the methodology of improving recommendation system by measuring interests of the user. Specially, this study proposes the method to quantify interests of the user by analyzing user's internet usage patterns, and to predict user's repurchase based upon the discovered preferences. There are two important modules in this study. The first module predicts repurchase probability of each category through analyzing users' purchase history. We include the first module to our research scope for comparing the accuracy of traditional purchase-based prediction model to our new model presented in the second module. This procedure extracts purchase history of users. The core part of our methodology is in the second module. This module extracts users' interests by analyzing news articles the users have read. The second module constructs a correspondence matrix between topics and news articles by performing topic modeling on real world news articles. And then, the module analyzes users' news access patterns and then constructs a correspondence matrix between articles and users. After that, by merging the results of the previous processes in the second module, we can obtain a correspondence matrix between users and topics. This matrix describes users' interests in a structured manner. Finally, by using the matrix, the second module builds a model for predicting repurchase probability of each category. In this paper, we also provide experimental results of our performance evaluation. The outline of data used our experiments is as follows. We acquired web transaction data of 5,000 panels from a company that is specialized to analyzing ranks of internet sites. At first we extracted 15,000 URLs of news articles published from July 2012 to June 2013 from the original data and we crawled main contents of the news articles. After that we selected 2,615 users who have read at least one of the extracted news articles. Among the 2,615 users, we discovered that the number of target users who purchase at least one items from our target shopping mall 'G' is 359. In the experiments, we analyzed purchase history and news access records of the 359 internet users. From the performance evaluation, we found that our prediction model using both users' interests and purchase history outperforms a prediction model using only users' purchase history from a view point of misclassification ratio. In detail, our model outperformed the traditional one in appliance, beauty, computer, culture, digital, fashion, and sports categories when artificial neural network based models were used. Similarly, our model outperformed the traditional one in beauty, computer, digital, fashion, food, and furniture categories when decision tree based models were used although the improvement is very small.